Non-Parametric Causality Detection: An Application to Social Media and Financial Data

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about the statistical relationships among the variables of the study and can effectively control a large number of factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201

Probabilistic Matching: Causal Inference under Measurement Errors

The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be inferred from other observed attributes. In such cases, inaccuracies of the applied inference methods will result in noisy outputs. In this study, we propose a novel approach for causal inference when one or more key variables are noisy. Our method utilizes the knowledge about the uncertainty of the real values of key variables in order to reduce the bias induced by noisy measurements. We evaluate our approach in comparison with existing methods both on simulated and real scenarios and we demonstrate that our method reduces the bias and avoids false causal inference conclusions in most cases.Comment: In Proceedings of International Joint Conference Of Neural Networks (IJCNN) 201

Investigating causality in human behavior from smartphone sensor data: a quasi-experimental approach

Smartphones and wearables have become an indispensable part of our daily life. Their improved sensing and computing capabilities bring new opportunities for human behavior monitoring and analysis. Most work so far has been focused on detecting correlation rather than causation among features extracted from smartphone data. However, pure correlation analysis does not offer sufficient understanding of human behavior. Moreover, causation analysis could allow scientists to identify factors that have a causal effect on health and well-being issues, such as obesity, stress, depression and so on and suggest actions to deal with them. Finally, detecting causal relationships in this kind of observational data is challenging since, in general, subjects cannot be randomly exposed to an event. In this article, we discuss the design, implementation and evaluation of a generic quasi-experimental framework for conducting causation studies on human behavior from smartphone data. We demonstrate the effectiveness of our approach by investigating the causal impact of several factors such as exercise, social interactions and work on stress level. Our results indicate that exercising and spending time outside home and working environment have a positive effect on participants stress level while reduced working hours only slightly impact stress

Towards Flexibility and Accuracy in Space DTN Communications

ABSTRACT Although Interplanetary Telecommunications rely on preconfigured contact schedules to make routing decisions, there is a lack of appropriate mechanisms to notify the network about contact plan changes. In order to fill this gap, we propose and evaluate a framework for disseminating information about queueing delays and link disruptions. In this context, we present such a mechanism, focusing not only on its functional properties, but rather on its impact objectives: to improve accuracy and routing performance. Supportively, we couple this mechanism with a DTN-compatible protocol, namely Contact Plan Update Protocol (CPUP), which implements our dissemination policy. Through simulation of space scenarios we show that accuracy can be significantly improved in all cases while routing performance can achieve a wide range, from minor through to significant gains, conditionally

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Queue-Management Architecture for Delay Tolerant Networking

Part 8: Emerging ContributionsInternational audienceDuring the last years, the interest in Delay/Disruption Tolerant Networks has been significantly increased, mainly because DTN covers a vast spectrum of applications, such as deep-space, satellite, sensor and vehicular networks. Even though the Bundle Protocol seems to be the prevalent candidate architecture for delay-tolerant applications, some practical issues hinder its wide deployment. One of the functionalities that require further research and implementation is DTN queue management. Indeed, queue management in DTN networks is a complex issue: loss of connectivity or extended delays, render occasionally meaningless any pre-scheduled priority for packet forwarding. Our Queue-management approach integrates connectivity status into buffering and forwarding policy, eliminating the possibility of stored data to expire and promoting applications that show potential to run smoothly. Therefore, our approach does not rely solely on marked priorities but rather on active networking conditions. We present our model analytically and compare it with standard solutions. We then develop an evaluation tool by extending ns-2 modules and, based on selective scenarios primarily from Space Communications, we demonstrate the suitability of our model for use in low-connectivity/high-delay environments